Prediction is a statistical estimation process where one or more random variables are estimated from observations of other random variables. It is called prediction when the variables to be estimated are in some sense associated with the “future” and the observable variables are associated with the “past”. One of the simplest, prevalent prediction techniques is linear prediction. Linear prediction consists for instance in predicting a vector from another vector. The most common use of prediction is the estimation of a sample of a stationary random process (i.e. a random stochastic process whose joint probability distribution does not change when shifted in time or space) from observations of several prior samples. Another application of prediction is, in image/video compression, when a block of pixels is estimated from an observed “prior” block of pixels comprised in a reference image (also called forward image). In this case, each predicted image (or picture or frame) is divided into non-overlapped rectangular blocks. Motion vectors (i.e. the vectors used for prediction that provides an offset from the coordinates in the predicted picture to the coordinates in a reference picture) of each block are derived using Motion Estimation (ME) in the reference picture. Then, each block is predicted using Motion Compensation (MC) with reference to the corresponding block in the reference frame pointed by the derived motion vectors. Both ME and MC are methods known to the person skilled in the art. This method may help eliminating redundancy information and, consequently, fewer bits may be needed to describe the residual (which is the difference between the original and the predicted block). However, such ME/MC prediction method is actually not the ultimate solution for predicting future frames as it is based on the assumption that the captured moving object is performing translation motion, which is not always true. Besides, for the estimation of images involving non-Gaussian processes, the ME/MC technique cannot fully squeeze out all possible information about the past that will help predicting future frames.
Today there is a need for an image prediction solution that can easily be implemented on the existing communication infrastructures, overcoming the drawbacks of the prior art.